Building an Intelligent QA/Chatbot for Transportation with LangChain and Open Source LLMs

TR Number

Date

2025-05-02

Journal Title

Journal ISSN

Volume Title

Publisher

Virginia Tech

Abstract

Description

We hope to provide a platform that can offer context-aware assistance geared towards the user’s questions. Through seamless follow-up questions and stored conversation contexts, we believe that traffic engineers will have a tool that effectively helps them based on their situation. The ultimate goal is to improve the workflow of traffic engineers by simultaneously improving their productivity when dealing with simulation tools.

Keywords

LLM, LangChain, Retrieval Augmented Generation (RAG), ChromaDB, Vector Store, Embeddings, Prompt Engineering, Memory Module, Natural Language Processing (NLP), Semantic Search, Traffic Simulation, Open Source LLMs, Google Colab, HuggingFace, LlamaCpp, React, Flask, Chatbot Development, User Authentication, Conversation History, Web App, Text Classification, Machine Learning, Backend Development, Frontend Development, Python, SQL

Citation